Summary of Dager: Exact Gradient Inversion For Large Language Models, by Ivo Petrov and Dimitar I. Dimitrov et al.
DAGER: Exact Gradient Inversion for Large Language Models
by Ivo Petrov, Dimitar I. Dimitrov, Maximilian Baader, Mark Niklas Müller, Martin Vechev
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed algorithm, DAGER, tackles the challenge of recovering private client data in federated learning by exploiting the low-rank structure of self-attention layer gradients and discrete token embeddings. This allows for exact recovery of full batches of input text without any prior knowledge about the data. The authors demonstrate the effectiveness of DAGER on large language models, achieving faster speeds (20x), larger batch sizes (10x), and better reconstruction quality (ROUGE-1/2 > 0.99) compared to previous attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way for many devices to work together without sharing their private data. But some clever hackers found a way to get the original data back from the server just by looking at how the models changed. This doesn’t work well with text, but now there’s an algorithm called DAGER that can exactly recover whole batches of text without knowing what it is ahead of time. It works by finding patterns in how words are related and using those patterns to figure out if a piece of text belongs to one of the devices or not. This is important because it makes sure our data stays safe even when we’re sharing models with others. |
Keywords
» Artificial intelligence » Federated learning » Rouge » Self attention » Token
